Gal A. Kaminka's Publications

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Obtaining Scalable and Accurate Classification in Large Scale Spatio-temporal Domains

Igor Vainer, Gal A. Kaminka, Sarit Kraus, and Hamutal Slovin. Obtaining Scalable and Accurate Classification in Large Scale Spatio-temporal Domains. Knowledge and Information Systems, 29(3):527–564, 2011.

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Abstract

We present an approach for learning models that obtain accurate classification of data objects, collected in large scale spatio-temporal domains. The model generation is structured in three phases: spatial dimension reduction, spatio-temporal features extraction, and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. We explore model generation based on the combinations of techniques from each phase. We apply the introduced methodology to data-sets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the resulting classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI is currently the best technique enabling simultaneous high spatial ($10,000$ points) and temporal ($10 ms$ or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities. The effectiveness of our methodology is further explored on a data-set from the hurricanes domain, and a promising direction, based on the preliminary results of hurricane severity classification, is revealed.

BibTeX

@Article{kais11,
author = {Igor Vainer and Gal A. Kaminka and Sarit Kraus and Hamutal Slovin},
title = {Obtaining Scalable and Accurate Classification in Large Scale Spatio-temporal Domains},
journal = KAIS,
year = {2011},
OPTkey = {},
volume = {29},
number = {3},
pages = {527--564},
OPTmonth = {},
OPTnote = {},
OPTannote = {},
OPTurl = {},
OPTdoi = {10.1007/s10115-010-0348-2},
OPTissn = {},
OPTlocalfile = {},
  wwwnote = {}, 
abstract = {
We present an approach for learning models that obtain accurate classification 
of data objects, collected in large scale spatio-temporal domains. 
The model generation is structured in three phases: spatial dimension 
reduction, spatio-temporal features extraction, and feature selection. 
Novel techniques for the first two phases are presented, with two 
alternatives for the middle phase. We explore model generation based 
on the combinations of techniques from each phase. We apply the introduced 
methodology to data-sets from the Voltage-Sensitive Dye Imaging (VSDI) 
domain, where the resulting classification models successfully decode 
neuronal population responses in the visual cortex of behaving animals. 
VSDI is currently the best technique enabling simultaneous high spatial 
($10,000$ points) and temporal ($10\, ms$ or less) resolution imaging 
from neuronal population in the cortex. We demonstrate that not only 
our approach is scalable enough to handle computationally challenging 
data, but it also contributes to the neuroimaging field of study with 
its decoding abilities. The effectiveness of our methodology is further 
explored on a data-set from the hurricanes domain, and a promising 
direction, based on the preliminary results of hurricane severity classification, is revealed. }
}

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